JP5916738B2 - Method and system for providing device-induced errors using a subsampling scheme - Google Patents

Description

This application relates to and claims the benefit of the earliest valid filing date (s) from the application (s) listed below ("Related Application") ( For example, any and all parent applications, grandfather applications, great-grandfather applications, etc. of the related application (s), the earliest priority date other than the provisional patent application, or US 35 USC 351.9 (E) claims profit based on paragraph).

  Because of the non-statutory requirements of the USPTO, this application was filed on September 30, 2010, with the name of “METHOD TO REDUCE NUMBER OF OF MEASUREMENT POINTS USED FOR TIS COLLECTION” as the inventor. Consists of provisional patent application 61 / 388,427, US patent application (regular application).

  The present invention relates generally to a method and system for providing device-induced error (TIS) values across a semiconductor surface, and more particularly to generate and execute a TIS subsampling scheme for a semiconductor wafer in cooperation with an interpolation process. It relates to a method and a system.

  As the dimensions of semiconductor devices and components continue to decrease, the need for increased alignment control between different layers or features within a single layer of a given sample continues to increase. In semiconductor processing, semiconductor-based devices may be produced by fabricating a series of layers on a substrate, some or all of the layers including various structures. The relative position of these structures, both within the monolayer and with respect to structures in other layers, is critical to device performance. Examples of semiconductor manufacturing processes include, but are not limited to, chemical mechanical polishing (CMP), etching, deposition, and ion implantation. Multiple semiconductor devices may be manufactured in one configuration on a single semiconductor wafer and then separated into individual semiconductor devices.

  Metrology processes are used at various steps during the semiconductor manufacturing process to monitor and control one or more semiconductor layer processes. For example, a metrology process may be used to measure one or more characteristics of the wafer, such as the dimensions of features formed on the wafer during the process step (eg, line width, thickness, etc.) Quality can be determined by measuring one or more features. One such feature includes overlay errors.

  Overlay measurements generally indicate how accurately the first patterned layer aligns with the second patterned layer disposed above or below it, or the first pattern. Designates how accurately the second pattern placed on the same layer is aligned. Overlay error is typically determined with an overlay target having a structure formed on one or more layers of a workpiece (eg, a semiconductor wafer). If a layer or pattern of a given semiconductor device is not properly formed, structures on one layer or pattern are likely to be offset or misaligned with structures on other layers or patterns. Mismatch between any patterns used at different stages of a semiconductor integrated circuit manufacturing is known as "overlay error".

  Further, if a measured feature, such as a wafer overlay error, is unacceptable (eg, out of a predetermined range for the feature), the measurement of one or more features can be used to process Modify one or more parameters of the process so that the additional wafers manufactured by have acceptable characteristics.

  In the case of overlay errors, overlay measurements may be used to correct the lithographic process to keep the overlay measurements within desired limits. For example, overlay measurements can be sent by an operator to send "correction values" and other statistics to analysis routines that can be used to better align lithography tools used for wafer processing. May be used.

  In a general sense, metrology applications such as overlay metrology require high quality optics to satisfy the requirements of advanced lithographic processes. In the case of overlay metrology, optical defects (eg, deviations) in the optical components of the implementation system can cause device-induced errors (TIS). In this approach, optical defects in the optical system can cause errors in the measured overlay relative to the actual overlay. For example, the presence of optical deviations in the metrology optical column can cause TIS. A standard measurement of TIS is to measure the overlay at a first position, then rotate the wafer 180 degrees and repeat the overlay measurement.

  However, there are many disadvantages to using metrology processes and tools to measure one or more characteristics of a wafer for process monitoring and control applications. For example, most metrology tools are relatively slow, especially compared to inspection systems. Thus, metrology processes are often performed at a single location or a limited number of locations on the wafer so that measurement results can be obtained in a relatively convenient manner. However, many processes used to manufacture semiconductor devices produce wafers that have feature (s) that vary across the surface of the wafer. Therefore, using metrology measurements performed at a single location or a limited number of locations on the wafer can allow for the wafer feature (s) so that the process can be accurately monitored and controlled. ) May not provide sufficient information. Therefore, the sampling plan of the measurement process can have a significant impact on the significance and benefit of the measurement results.

  In practical terms, all optical metrology systems generate some device-induced error. Thus, TIS must be corrected during the semiconductor device manufacturing process, resulting in increased processing time and cost. These inefficiencies are due to the fact that a single TIS measurement requires two overlay measurements, a first overlay measurement in the zero degree wafer direction and a second overlay measurement in the 180 degree wafer direction. It gets worse.

US Pat. No. 7,725,208 US Pat. No. 7,385,699 US Patent Application Publication No. 2007-0258074

  Therefore, while reducing the measurement of selected wafers, reducing the loss of measurement information by utilizing approximation methods to provide sufficient TIS information for unmeasured sampling locations, It may be desirable to provide a method and / or system that provides a more effective TIS sampling scheme.

A method for providing device-induced error values over a semiconductor surface is disclosed. In one aspect, the method is to measure an instrument-induced error (TIS) on at least one wafer in a lot of wafers via a holistic sampling process, wherein the holistic sampling process has a TIS of at least 1 Measuring at each measurement location of each field of one wafer, and randomly generating a plurality of sub-sampling schemes, wherein the number of fields sampled by each sub-sampling scheme is: pre-selected, each set of sub-sampling method randomly generated to have the same number of sampled fields, and it produced, each of the sub-sampling method produced a TIS to each random Measure in place and each randomly generated sub From a sampling scheme to a set of TIS values for each randomly generated sub-sampling scheme using TIS measurements, for each randomly generated sub-sampling scheme. Each set of values approximates the TIS value for each location not included in the randomly generated subsampling scheme, using the TIS measured at each location of the randomly generated subsampling scheme. Approximation, calculated using an interpolation process configured as follows, and comparing each calculated set of TIS values with the measured TIS of the all-knowledge sampling process Determining a sampling scheme, wherein a sub-sampling scheme defines a set of measurement locations on at least one wafer. No, and it is determined may include but is not limited thereto.

In another aspect, the method is to generate a device-induced error (TIS) sub-sampling scheme, wherein the TIS sub-sampling scheme is one or more statistical criteria, a selected number of sampling locations, and A TIS subsampling scheme defined to utilize a selected model type for TIS dependence across the surface of a semiconductor wafer includes a set of measurement locations for the semiconductor wafer and a generated TIS By determining the first set of TIS values by measuring the TIS at each of the sub-sampling measurement locations and using an interpolation process, a set of TIS values not included in the generated TIS sub-sampling scheme. For each location, a second set of TIS values is determined by approximating the TIS, the interpolation process Determining, for each set of locations not included in the generated TIS sub-sampling scheme, to use the first set of TIS values to calculate an approximate TIS value; Although it may include, it is not limited to this.

  In another aspect, the method is to measure an overlay on at least one wafer of a lot of wafers via an all-knowledge sampling process in a first wafer direction, the all-knowledge sampling process comprising at least one Identifying a set of measurement locations having an overlay value between the first overlay value and the second overlay value, including measuring an overlay at each measurement location of each field of the wafer Generating a sub-sampling scheme, wherein the sub-sampling scheme includes generating a set of measurement locations for at least one wafer and overlaying the at least one wafer on the sub-sampling scheme. In the direction of the second wafer rotated 180 degrees with respect to the direction of the first wafer at each measurement location And a first set of instrument-induced errors (TIS) for a set of sub-sampling measurement locations, an overlay measured in the first wafer direction, and a first wafer direction A set of at least one wafer that is not included in the generated sub-sampling scheme using an interpolation process to determine using an overlay measured in the direction of the second wafer rotated 180 degrees For each of the measurement locations, a second set of TIS values is determined by approximating the TIS, and the interpolation process is performed for the set of locations not included in the generated TIS subsampling scheme. For each, it may include, but is not limited to, utilizing a first set of TIS values to calculate an approximate TIS value.

  In another aspect, the method is to measure an overlay on at least one wafer of a lot of wafers via an all-knowledge sampling process in a first wafer direction, the all-knowledge sampling process comprising at least one Measuring and including a first set of process tool correction values, including measuring an overlay at each measurement location in each field of the wafer, to one or more of the overlays measured through an omni-directional sampling process Utilizing the results to generate, wherein the first set of process tool correction values includes a calculated process tool correction value for each measurement location of each field of at least one wafer, and at least A first set of process tools with process tool correction values associated with the analyzed measurement location of one wafer A positive value is calculated using a measured overlay of all measurement locations on at least one wafer, and a second set of process tool correction values is measured via an omni-directional sampling process. Generating a second set of process tool correction values for each measurement location of each field of at least one wafer A second set of process tool correction values, including process tool correction values and related to an analyzed measurement location of at least one wafer, includes at least one wafer, except for the analyzed measurement location. The first generated set of process tool correction values calculated using the measured overlay of all measurement locations Generating a sub-sampling scheme by comparing with a second generated set of process tool correction values, the sub-sampling scheme including a set of measurement locations, The set includes a selected number of sub-sampling measurement locations and has the largest difference between the first generated set of process tool correction values and the second generated set of process tool correction values. A selected number of measurement locations of the at least one wafer form a set of sub-sampling measurement locations, and at each generated sub-sampling measurement location, in the first wafer direction Measuring an overlay on at least one wafer in a second wafer direction rotated 180 degrees relative to the subsampling sub For a set of sampled measurement locations, a first set of instrument-induced error (TIS) values is applied to the overlay measured in the first wafer direction and the first rotated 180 degrees relative to the first wafer direction. For each of a set of locations on at least one wafer that is not included in the generated sub-sampling scheme, using an overlay process measured using two measured wafer directions Determining a second set of TIS values by approximating the TIS, and the interpolation process is approximated for each set of locations not included in the generated TIS subsampling scheme. Using a first set of TIS values to calculate the TIS values may include, but is not limited to:

  It should be understood that both the foregoing summary and the following details are exemplary and explanatory only and are not necessarily limitations of the invention as claimed. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with an overview, serve to explain the principles of the invention.

  Many of the advantages of the present disclosure may be better understood by those of ordinary skill in the art with reference to the accompanying drawings.

1 is a plan view of a semiconductor wafer having defined fields. FIG. 1 is a plan view of an individual field of a semiconductor wafer showing a plurality of targets in the field. 1 is a block diagram illustrating a system for providing device-induced error values across a semiconductor surface, according to one embodiment of the invention. FIG. 3 is a flow diagram illustrating a method for providing device-induced error values across a semiconductor surface, according to one embodiment of the invention. FIG. 6 illustrates an exemplary subsampling scheme suitable for implementation in the present invention. 3 is a flow diagram illustrating a method for providing device-induced error values across a semiconductor surface, according to one embodiment of the invention. 3 is a flow diagram illustrating a method for providing device-induced error values across a semiconductor surface, according to one embodiment of the invention. 3 is a flow diagram illustrating a method for providing device-induced error values across a semiconductor surface, according to one embodiment of the invention.

  Reference will now be made in detail to the disclosed subject matter, which is illustrated in the accompanying drawings.

  Referring generally to FIGS. 1A-7, a method and system for providing device-induced error values across a semiconductor surface is described in accordance with the present disclosure. Utilizing an instrument-induced error (TIS) sub-sampling scheme in combination with one or more interpolation processes may improve semiconductor wafer TIS determination and subsequent process tool correction. Traditionally, overlay metrology is used to determine the TIS and is performed at certain locations on the semiconductor wafer. The present invention works in conjunction with an interpolation process (eg, wavelet analysis, spline interpolation, polynomial interpolation, or neural network interpolation) for a sub-sampling scheme (eg, an optimized, improved or reduced measurement sampling scheme). And methods and systems for providing device-induced errors across semiconductor surfaces. The interpolation process allows an estimate of the TIS in the unmeasured field of the tested semiconductor wafer. The combination of fewer measurement locations and interpolation processes due to the sub-sampling scheme allows the user to collect accurate TIS information more effectively and acceptably, thereby increasing the overall semiconductor manufacturing process.

  As used throughout this disclosure, the term “wafer” generally refers to the form of a substrate of a semiconductor or non-semiconductor material. For example, the semiconductor or non-semiconductor material may include, but is not limited to, single crystal silicon, gallium arsenide, and indium phosphide. The wafer may include one or more layers. For example, such layers may include, but are not limited to, resists, dielectric materials, conductive materials, and semiconductor materials. Many different types of such layers are known in the art, and as used herein, the term wafer encompasses wafers on which all types of such layers can be formed. Is intended to be.

  A normal semiconductor process includes wafer processing in lot units. As used herein, a “lot” is a group of wafers that are processed together (eg, a group of 25 wafers). Each wafer in the lot includes a number of exposure fields consisting of lithographic processing tools (eg, steppers, scanners, etc.). There may be multiple dies in each field. A die is a functional unit that eventually becomes a single chip. On the product wafer, overlay measurement marks are usually placed in the scribe line area (for example, in the four corners of the field). This is the area where there is no normal circuit near the periphery of the exposure field (and outside the die). In some cases, overlay targets are placed in the street. Street is the area between the dies but not around the field. Since this area is very necessary for the circuit, it is quite rare that the overlay target is placed on the product wafer in the prime die area. However, engineering and characteristic wafers (not product wafers) typically have many overlay targets throughout the center of the field without such limitations. Due to the spatial separation between the “scribe line” metrology mark and the prime die circuit, there is a discrepancy between what is measured on the product wafer and what is to be optimized. Development of both scribe line measurement marks and their interpretation is required.

  The layer or layers formed on the wafer may or may not be patterned. For example, the wafer may include a plurality of dies, each having repeatably patterned features. Formation and processing of such layer material may ultimately result in a completed device. Many different types of devices may be formed on a wafer, and as used herein, the term wafer refers to a wafer on which any type of device known in the art is manufactured. It is intended to include.

FIG. 2 illustrates a system 200 that provides device-induced error (TIS) values across a semiconductor surface using a subsampling scheme with smart interpolation. In one embodiment, the system 200 may include a metrology system 202, such as a metrology system configured to perform overlay metrology, or CD metrology at an identified location on a semiconductor wafer. Measurement system 202 may include any suitable measurement system known in the art. For example, the metrology system 202 may include a metrology system 205 configured to perform device-induced error (TIS) measurements on the semiconductor wafer 204. The TIS may be defined as follows:

  Where OVL (0 °) represents the overlay measured at the first position and OVL (180 °) is the overlay measured after rotating the sample 180 degrees relative to the first position. is there.

  In this sense, metrology system 205 may be configured to measure overlay error for a set of measurement locations on wafer 204. If the wafer 204 is then rotated 180 degrees, the metrology system 205 may again measure the overlay error at the same set of measurement locations. Overlay measurements may be sent to computer system 206 and TIS may be calculated utilizing an algorithm consistent with Equation 1 above.

  In further embodiments, the measurement system 202 may be configured to accept instructions from another subsystem of the system 200 to execute a specified measurement plan. For example, the metrology system 202 may accept instructions from one or more computer systems 206 of the system 200. Upon receiving instructions from computer system 206, metrology system 202 may perform overlay metrology at various locations on semiconductor wafer 204 identified in the provided instructions. As discussed in further detail herein, the instructions provided by computer system 208 may include a subsampling scheme, which is a selected subset of available measurement locations on semiconductor wafer 204. May be input to a metrology tool 202 (eg, metrology tool 205) to measure the TIS.

  In one aspect, the one or more computer systems 206 of the system 200 are selected by a user-entered number of measurement locations, a user-entered model for a TIS across the wafer 204 (eg, a polynomial basis model), and selected. The sub-sampling scheme may be generated based on the optimal sampling scheme determined using the optimal criterion (for example, A optimal criterion, B optimal criterion, D optimal criterion, etc.).

  In one embodiment, the one or more computer systems 206 may select the user-selected number of sub-sampling locations and the user-selected TIS model type entered via the system 200 user interface (not shown). May be configured to receive. The one or more computer systems 206 may be further configured to calculate a TIS subsampling scheme using the optimal design algorithm 212. In this sense, the optimal design algorithm 212 is input to determine an optimized subsampling scheme for a selected set of statistical criteria (eg, A optimal criteria, B optimal criteria, D optimal criteria, etc.). Utilize a number of sampling locations and TIS model types.

  In another aspect, one or more computer systems 206 of system 200 may be configured to generate a sub-sampling scheme based on an analysis of omni-directional sampling of a first lot of test wafers. In one embodiment, the one or more computer systems 206 are a set of measurement results that are performed by the metrology system 202 (eg, the metrology system 205) in a holistic sampling process of one or more wafers in the test lot. May be configured to receive. The one or more computer systems 206 may be further configured to calculate a set of TIS values using the measurement results received from the omni-directional sampling process. The one or more computer systems 206 use a random subsampling algorithm 214 to measure multiple sets of wafers (eg, across wafers, across individual fields, or both). It may be configured to generate randomly, and the user inputs the size of each sub-sampling scheme (number of measurement locations for each scheme) and the number of sub-sampling schemes. Using these randomly selected locations, the computer system 206 then inputs the TIS values measured from each location of the plurality of TIS subsampling schemes into an interpolation algorithm, thereby allowing the entire wafer and / or Multiple modeled sets of TIS across fields may be calculated. The interpolation algorithm may then approximate the TIS across wafers and / or fields of wafers that are not included in a randomly selected set of sampling locations for each sub-sampling scheme. The modeled set of TISs (ie, values taken from the interpolated random location selection) are then compared by computer system 206 for comparison with the set of TIS values obtained in the omni-directional sampling process. May be used. The computer system 206 then determines which sub-sampling scheme best minimizes the difference between the modeled TIS and the measured TIS obtained through omni-directional sampling. A sampling method may be determined. In another embodiment, the computer system 206 reduces which subsampling scheme reduces the difference between the modeled TIS and the measured TIS obtained through omni-directional sampling below a selected threshold level. The sub-sampling method may be determined by determining whether or not to do so.

  In another aspect, one or more computer systems 206 of system 200 may use a critical metric 216 algorithm to detect a first rotational direction between the first overlay value and the second overlay value (ie, It may be configured to generate a sub-sampling scheme by identifying a set of measurement locations that have an overlay value at zero degrees). In one embodiment, the system 200 may identify N measurement locations that display the largest overlay value. In another aspect, the system 200 may identify N measurement locations that display the smallest overlay value. In a further embodiment, the system 200 may identify N measurement locations that display overlay values between the first overlay level and the second overlay level. The system 200 may then measure a 180 degree overlay at each location of the subsampling scheme using the identified subsampling scheme. The computer system 206 may then calculate a TIS for each sub-sampling measurement location. Further, using pre-programmed algorithms, TIS for measurement locations not included in the sub-sampling scheme can be performed by one or more interpolation processes performed by one or more computer systems 206 of system 200. May be approximated.

  In another aspect, one or more computer systems 206 of system 200 use a critical metric 216 algorithm to identify a set of measurement locations that have the greatest impact on a set of process tool corrections. Thus, it may be configured to generate a subsampling scheme. The system 200 may then measure a 180 degree overlay at each location of the subsampling scheme using the identified subsampling scheme. The computer system 206 may then calculate a TIS for each sub-sampling measurement location. Further, using pre-programmed algorithms, TIS for measurement locations not included in the sub-sampling scheme may involve one or more interpolation processes performed by one or more computer systems 206 of system 200. May be approximated.

  It should be appreciated that the various steps described throughout this disclosure may be performed by a single computer system 206, or alternatively, multiple computer systems 206. Further, different subsystems of system 200, such as metrology system 202, may include a suitable computer system to perform at least some of the steps described above. Therefore, the above description should not be construed as limiting, but merely as exemplifications. Further, the one or more computer systems 206 may be configured to perform any other step (s) of any method embodiments described herein.

  In another aspect, one or more computer systems 206 may then send instructions to measurement system 202 (eg, measurement system 205) indicating a generated sub-sampling scheme such as those described above. Further, the computer system 206 may be configured to generate a sampling scheme in accordance with any embodiment described herein.

  In another aspect, the one or more computer systems 206 may send instructions to the one or more process tools that indicate a correction value for the set of process tools based on the measured overlay and TIS. Good. Further, the transmitted instructions may include information indicating overlay, focus, and capacity correction values. Further, the one or more computer systems 206 may be configured to perform any other step (s) of any method embodiments described herein.

  In other embodiments, the computer system 206 may be communicatively connected to the metrology system 202 or another process tool in any manner known in the art. For example, one or more computer systems 206 may be coupled to a computer system of measurement system 202 (eg, a computer system of measurement system 205) or to a computer system of process tools. In another example, metrology system 202 and another process tool may be controlled by a single computer system. In this manner, the computer system 206 of the system 200 may be combined into a single metrology process tool computer system. Further, the computer system 206 of the system 200 can be derived from other systems (eg, test results from an inspection system, measurement results from a measurement system, or process tool correction values calculated from a system such as KLA-Tencors' KT Analyzer). It may be configured to receive and / or acquire data or information via a transmission medium that may include wired and / or wireless portions. In this manner, the transmission medium may serve as a data link between computer system 206 and other subsystems of system 200. Further, the computer system 206 may transmit data to an external system via a transmission medium. For example, the computer system 206 may send the generated sub-sampling scheme or set of process tool correction values to a separate measurement system that exists independently of the described system 200.

  Computer system 206 may include, but is not limited to, a personal computer system, mainframe computer system, workstation, image computer, parallel processor, or any other device known in the art. In general, the term “computer system” may be broadly defined to encompass any device having one or more processors that execute instructions from a storage medium.

  A method and system for generating and providing an optimized sampling scheme using overlay measurements in a computer system is generally described in US patent application Ser. No. 12 / 107,346, filed Apr. 22, 2008. And are hereby incorporated by reference.

  Program instructions 210 that implement methods, such as those described herein, may be transmitted across the carrier medium 208 or stored on the carrier medium 208. The carrier medium may be a transmission medium such as a wired, cable, or wireless transmission link. The carrier medium may also include a storage medium such as a read-only memory, a random access memory, a magnetic or optical disk, or a magnetic tape.

  The embodiment of the system 200 shown in FIG. 2 may be further configured as described herein. In addition, the system 200 may be configured to perform any other step (s) of any method embodiment (s) described herein.

  FIG. 3 is a flow diagram illustrating the steps performed in a method for providing device-derived error values across a semiconductor surface using a sub-sampling and sampling scheme with smart interpolation. In one aspect, it is recognized that the data processing steps of process flow 300 may be performed via pre-programmed algorithms that are performed by one or more processors of computer system 206. However, those skilled in the art will recognize that the system 200 should not be construed as limited to the process 300 as it is contemplated that various system configurations may execute the process flow 300.

  In a first step 302, a first device-induced error (TIS) measurement may be performed on at least a first wafer of a first lot of wafers using an all-knowledge sampling process. The omni-directional sampling process may include measuring one or more TIS values at each measurement location of the first wafer in the wafer lot. For example, the omni-directional sampling process may measure the TIS (eg, measuring the overlay at 0 degrees and 180 degrees and calculating the TIS at each measurement location of each field of the first wafer of the first lot of wafers. ) May be measured.

  The method includes performing a TIS measurement on one or more wafers in a lot of wafers at all measurement spots on the one or more wafers. This step is referred to herein as “omni-knowledge sampling”. As shown in FIG. 1A, in one embodiment, all measurement spots include all fields 104 on one or more wafers 102. For example, as shown in FIG. 1A, the wafer 102 has a plurality of fields 104 formed thereon. Although a particular number and arrangement of fields 104 on the wafer 102 is shown in FIG. 1A, the number and arrangement on the wafer may vary depending on, for example, the devices formed on the wafer. Measurements may be performed on all fields 104 formed on the wafer 102 and on all fields on other wafers in at least one lot. For example, the measurement may be performed at least once for each field formed on all wafers in at least one lot. Measurements may be performed on device structures formed in the field and / or on test structures formed in the field. In addition, the measurements performed within each field may include all measurements performed during the metrology process (eg, one or more different measurements).

  In another embodiment, all measurement steps measured in an omni-directional sampling process may include all targets on one or more wafers in at least one lot. For example, as shown in FIG. 1A, the field 104 formed on the wafer 102 may include a target 106. Although a particular number and arrangement of targets 106 in field 104 is shown in FIG. 1B, the number and arrangement of targets 106 in field 104 may vary depending on, for example, the devices formed on wafer 102. Also good. Target 106 may include a device structure and / or a test structure. In this embodiment, therefore, measurements may be performed on all targets 106 formed in each field 104. In addition, the measurement may be performed at least once for each target formed in the field 104. Measurements may also include all measurements performed during the measurement process (eg, one or more different measurements). Those skilled in the art will recognize that the target 106 and field 104 of FIGS. 1A and 1B are not shown to scale because the field and target are enlarged relative to the wafer 102 for illustration.

  In another embodiment, all measurement spots include all fields and all targets on wafers in at least one lot. For example, each of the fields 104 formed and shown on the wafer 102 in FIG. 1A may include one or more targets, such as the target 106 shown in FIG. 1B. Accordingly, measurements may be performed on each target 106 in each field 104 formed on each wafer 102 in at least one lot.

  In the second step 304, a plurality of subsampling schemes may be randomly generated. In one aspect, the number of sampling locations (N) in each sub-sampling scheme may be pre-selected by the user, while those sampling location locations may be randomly generated. In one embodiment, multiple sub-sampling scheme sampling locations (eg, locations within a wafer or locations within each field of a wafer) may be randomly generated via a Monte Carlo simulation process. In another embodiment, the number of sampling locations sampled in the sub-sampling scheme may be pre-selected to be within the range of sub-sample locations. For example, the user may select a minimum and / or maximum number of sampling locations to be sampled. Then, after selecting the number of sampling locations or the range of sampling locations, the associated computer system may randomly select the location of the sub-sampling location for each sub-sampling scheme. In another embodiment, the computer system may randomly select measurement locations within a selected field that are analyzed in multiple sets of fields and subsequent process steps. In another aspect, the number of randomly generated subsampling schemes (M) may be preselected by the user. For example, the user may select in advance to generate 1000 randomly generated subsampling schemes.

  In one embodiment, each sub-sampling scheme may include a subset of the total wafer field. For example, as shown in FIG. 4, the subsampling scheme 402 may include a field 404 that is a subset of the total field 406 of wafers 400. With respect to FIG. 4, shaded area 404 represents a field that is included in the subsampling scheme, while unshaded area 406 represents a field that is not sampled in subsequent lots. Thus, a sub-sampling scheme may include fewer sampled fields than a given total number of wafer fields. In another embodiment, the subsampling scheme may include a subset of the total number of measurement spots (eg, overlay targets) with a single field of the wafer.

  In another embodiment, the user may select additional restrictions. For example, the pattern formed by the location of randomly generated sub-sampling sampling locations may be required to have a selected spatial symmetry, such as a 180 ° or 90 ° rotational symmetry.

  In a third step 306, the TIS may be measured at each location of each randomly generated sub-sampling scheme generated in step 304. It should be noted that the measurement process used to characterize the TIS associated with randomly generated sub-sampling measurement locations is described in 302 all-knowledge sampling steps.

  In a fourth step 308, a set of TIS values for each location of the randomly generated sub-sampling scheme that is not included in the sub-sampling scheme may be generated via an interpolation process. In this approach, the interpolation process is performed for the randomly selected measurement locations of step 304 to generate a set of approximate TIS values for locations not included in the randomly generated subsampling scheme. It may be applied to each. For example, for each of the randomly generated sub-sampling schemes, the interpolation process utilizes the TIS value obtained from the randomly selected field location of step 304 to determine the unsampled location (ie, step (Where not selected by the random selection of 304) may be used to calculate the TIS.

  In one embodiment, the interpolation process may include, but is not limited to, spline interpolation, polynomial interpolation, wavelet interpolation, or neural network interpolation process. In a general sense, any interpolation algorithm applied to a set of input values to calculate or model a set of output values may be implemented in the present invention.

  Examples of modeling used in the context of semiconductor metrology systems are US Pat. No. 6,704,661, US Pat. No. 6,768,967, US Pat. No. 6,867,866, US Pat. , 898,596, U.S. Patent No. 6,919,964, U.S. Patent No. 7,069,153, U.S. Patent No. 7,145,664, U.S. Patent No. 7,873,585 and U.S. Patent Application No. 12 / 486,830, all of which are incorporated herein by reference.

  In the fifth step 310, a preferred (or “selected”) sub-sampling scheme is formed using a combination of sub-sampling and interpolation, using the TIS values measured in the omni-knowledge sampling process of step 302. It may be determined by comparing with each of the sub-sampling scheme sets.

  In one embodiment, the comparison of the omni-directional sampling to the measurement set using the sub-sampling scheme is performed between the TIS of the omni-directional sampling process and the TIS of the sub-sampling / interpolating process of step 308 below a preselected level. Selecting a sub-sampling scheme configured to provide the difference may be included.

  In another embodiment, the sub-sampling scheme wherein the comparison between the omni-directional sampling and the measurement set using the sub-sampling scheme best minimizes the difference between the omni-directional TIS sampling and the TIS measurement set using the sub-sampling scheme. May be included. In this approach, the sub-sampling scheme that best minimizes the difference between the all-knowledge TIS sampling and the set of TIS measurements using the sub-sampling scheme is the preferred sampling scheme.

  In one embodiment, the preferred subsampling scheme may include an optimal sampling scheme. For example, when comparing a set of measurements using omni-directional sampling and a sub-sampling scheme, an optimal sampling scheme may be found by determining the optimal set of wafer measurement locations. Thus, the sub-sampling scheme may include a determined number of measurement locations and a determined number of locations of measurement locations. In general terms, the optimal sampling scheme is a subset sampling condition (eg, location and number of measurement locations) that best minimizes the difference between the measured TIS and the TIS approximated across the wafer. . Those skilled in the art will recognize that optimization of the subset sampling scheme may be accomplished using known techniques, including but not limited to D-optimal methods and Federov exchange algorithms.

  In another embodiment, a preferred subsampling scheme may include an enhanced sampling scheme. In a general sense, an improved sampling scheme allows a higher sampling rate than an optimized sampling scheme. In another embodiment, a preferred subsampling scheme may include a reduced sampling scheme. In a general sense, the reduced sampling scheme provides a lower sampling rate than the optimized sampling scheme. An optimal, enhanced and reduced subsampling scheme is generally described in US patent application Ser. No. 12 / 107,346, filed Apr. 22, 2008, which is hereby incorporated by reference. Incorporated.

  It should be recognized that the determined optimal sub-sampling scheme is not a requirement of the present invention. Rather, only a sufficient subsampling scheme needs to be determined in order to be implemented in the present invention. For example, a set of levels of accuracy may be required in one concept of the invention, and therefore the difference between the measured and modeled TIS of all-knowledge sampling is lower than this selected level. It is only necessary to provide a subsampling scheme that reduces to a value.

  It should further be appreciated that one or more sub-sampling schemes may be generated in any suitable format. For example, the file format may be configured such that the file format can be used by any measurement system or process tool known in the art.

  In a next step 312, subsequent TIS measurements on at least one wafer of a subsequent lot of wafers in each of the sub-sampling measurement location sets generated in step 310. In one aspect, the set of measurement locations may include a subset of the fields of the wafer and a subset of the measurement locations within each field of the wafer. TIS measurements performed on one or more wafers in subsequent lots may include TIS measurements similar to those performed in step 302. In this approach, as outlined in step 302, various measurement and measurement methods can be performed via a preferred sub-sampling scheme to the measurement locations (eg, selected fields and within each field). (Measurement location).

  In the next step 314, the TIS is approximated for each of the unmeasured measurement locations of at least one wafer in a subsequent lot that is not included in the preferred subsampling scheme using an interpolation process. Good. In one aspect, the interpolation process may use the measurement TIS as input for each of the preferred subsampling schemes for the measurement location. The interpolation process used to approximate field correction values not included in the subsampling scheme is similar to the interpolation process outlined in step 308.

  Interpolate across TIS values calculated in step 314, including TIS measurements of locations within the preferred subsampling scheme, and measurement locations not included in the preferred subsampling scheme (using the sampled location as input) It is further contemplated that the TIS values approximated in step 314, including the TIS values approximated by, may be combined into one table. A single modifiable table may be collected in any convenient computer file format.

  In another embodiment, the interpolation process of step 314 may implement a trainable history algorithm that is configured to incorporate information from previous interpolation processes. For example, information from wafers previously processed by system 200 (eg, wafers in the same lot or wafers in different lots) may be utilized during the current wafer interpolation process. In this regard, a history algorithm may be utilized to determine the limitations that exist on previous process wafers. For example, the history algorithm may utilize information about the TIS spatial dependence across previously processed wafers to improve the current wafer interpolation process. For example, information regarding the radial dependence or global or local maximum / minimum values of previously processed wafers may be utilized to limit the dependence of current processed wafers.

  In an additional step (not shown), following a subsequent TIS measurement on the wafer of a subsequent lot of wafers at one or more measurement locations of the set of selected sub-sampling measurement locations generated. An average of two or more TIS values obtained from one or more measurement locations may be calculated. The averaged two or more TIS values may then be assigned to all of the one or more measurement locations. For example, this process may be used to perform a spatial average of TIS across a semiconductor surface. This can be particularly advantageous when the TIS for a given wafer has (or is expected to have) relatively low variation across a portion of the wafer. In this approach, spatial averaging may be used to spatially average TIS values, which may be used to increase the overall.

  It is further contemplated that the single modifiable table described above may then be transmitted to one or more measurement or process tools to provide modifications to these and related systems.

  FIG. 5 is a flow diagram illustrating the steps performed in an alternative method of providing device-derived error values across a semiconductor surface using a sub-sampling and sampling scheme with smart interpolation. In an aspect, it is recognized that the data processing steps of process flow 500 may be performed via pre-programmed algorithms that are performed by one or more processors of computer system 206 of system 200. The However, one of ordinary skill in the art will recognize that the system 200 should not be construed as limited to the process 500, and that various system configurations may implement the process flow 500.

  In a first step 502, a device-induced error (TIS) subsampling scheme may be generated. In one aspect, the user may enter the number of sub-sampling measurement locations (N) into the system 200. In another aspect, the user may enter the type of model that is utilized to model the TIS across the wafer and / or field of the wafer. For example, the user may select a systematic polynomial based on the TIS model for TIS across wafers and / or fields.

  In another aspect, the TIS subsampling scheme is defined by one or more statistical criteria, an input N number of measurement locations, and an input TIS model type. In this regard, system 200 may utilize these inputs to determine the optimal TIS subsampling scheme. For example, the user may enter selected statistical criteria that the system 200 may use to optimize or nearly optimize the TIS subsampling scheme. For example, the selected statistical criteria may include, but is not limited to, A optimal criteria, B optimal criteria, D optimal criteria, G optimal criteria, I optimal criteria, V optimal criteria, and the like. Those skilled in the art will recognize that various statistical criteria may be utilized to optimize the TIS subsampling scheme of the present invention. The principle of design optimization may be applied to the optimization of the TIS sub-sampling scheme, and is generally described in Stephen Boyd and Lieven Vandenberghe, Convex Optimization, 7th edition, Cambridge University Press, 2009 Incorporated in the description.

  In one embodiment, the TIS subsampling scheme of step 502 may comprise an optimal sampling scheme. In another embodiment, the TIS subsampling scheme may include an improved sampling scheme. In another embodiment, a preferred sub-sampling scheme may include a reduced sampling scheme. An optimized, enhanced or reduced subsampling scheme is generally described in US patent application Ser. No. 12 / 107,346, filed Apr. 22, 2008, incorporated above by reference. It is.

  It should be appreciated that an optimal sub-sampling scheme is not a requirement of the present invention, as in process 300. Rather, only a sufficient subsampling scheme needs to be determined in order to be implemented in the present invention. For example, a set of levels of accuracy may be required in one concept of the invention, thus providing a sub-sampling scheme that provides a level of accuracy that needs to be given an input number of sampling locations. Only that is needed. In a general sense, it is anticipated that many of the determined subsampling schemes in step 302 may be essentially sub-optimized.

  In a second step 504, the first set of TIS values may be determined by measuring the TIS at each measurement location of the TIS subsampling scheme of step 502. In a general sense, the TIS measurement process of process 300 described earlier herein may be extended to step 504.

  In a third step 506, a second set of TIS values is determined by approximating the TIS for each location not included in the TIS subsampling scheme of step 502 using an interpolation process. May be. In one aspect, the interpolation process uses TIS values measured at each location of the sub-sampling scheme of step 502 as input. As in process 300, the interpolation process may include, but is not limited to, spline interpolation, polynomial interpolation, wavelet interpolation or neural network interpolation process. In a general sense, the interpolation process included in the process 300 previously described herein may be extended to step 506.

  In a general sense, various embodiments of process 300 should be construed as extending to process 500 unless otherwise stated.

  FIG. 6 is a flow diagram illustrating steps performed in an alternative method of providing device-derived error values across a semiconductor surface using a sub-sampling and sampling scheme with smart interpolation. In one aspect, it is recognized that the data processing steps of process flow 600 may be performed via pre-programmed algorithms that are executed by one or more processors of computer system 206. However, those skilled in the art will recognize that the system 200 should not be construed as limited to the process 600 and that various system configurations may implement the process flow 600.

  In step 602, an overlay measurement may be performed using an omni-directional sampling process at a first wafer orientation on at least a first wafer of a first lot of wafers. The omni-directional sampling process may include measuring one or more overlay error values at each measurement location of the first wafer of the wafer lot in the first wafer direction. For example, the first wafer direction may be referred to as the “zero degree direction” for purposes of this disclosure. For example, the omni-directional sampling process may include measuring overlay error in a zero degree direction at each measurement location of each field of the first wafer of the first lot of wafers. It should be appreciated that the all-knowledge sampling process is similar to the above-described all-knowledge sampling process in process flow 300. Accordingly, the omnidirectional sampling description of process 300 should be construed to extend to the present process step 602. For example, as shown in FIG. 1A, the all-knowledge overlay error sampling process may measure the overlay in all fields.

  In step 604, a subsampling scheme may be generated by identifying a set of measurement locations that have an overlay between the first overlay value and the second overlay value. In one embodiment, the sub-sampling scheme is generated by identifying a set of measurement locations having overlay values between the first overlay value and the maximum overlay value of the measured overlay value set of the wafer. May be. In this sense, in the omni-directional sampling process, the set of measurement locations that display the maximum overlay value of the set of measured overlay values on the wafer may be identified. These identified measurement locations may then serve as subsampling scheme locations.

  In another embodiment, the sub-sampling scheme is generated by identifying a set of measurement locations having overlay values between the first overlay value and the minimum overlay value of the measured set of overlay values on the wafer. May be. In this sense, in the omni-directional sampling process, the set of measurement locations that display the minimum overlay value of the set of measured overlay values on the wafer may be identified. These identified measurement locations may then serve as subsampling scheme locations.

  In another embodiment, the sub-sampling scheme may be generated by identifying a set of measurement locations that have an overlay value between the first overlay value and the second overlay value. For example, the system 200 may select N measurement locations that are within the standard deviation of the measured overlay's median overlay error value, as measured in an omni-directional sampling process. In another example, the system 200 may select N measurement locations that are within three standard deviations of the measured overlay median overlay error value measured in an omni-directional sampling process.

  In another embodiment, the number of sub-sampling measurement locations (N) may be selected by the user. In this sense, the system 200 may select N locations that display the maximum overlay utilized for the subsampling scheme. Alternatively, system 200 may select N locations that display the minimum overlay utilized for the subsampling scheme. In addition, the system 200 may select N locations within the range of overlay values utilized for the subsampling scheme.

  In another embodiment, the critical metric utilized to determine the subsampling scheme may be selected by the user. For example, the system 200 has N sub-sampling schemes of N locations that display an overlay between a first value and a second value, N locations that display a maximum overlay, and N locations that display a minimum overlay. It may be selected whether to be generated using the location of.

  In step 606, the overlay error may be measured at each measurement location of the sub-sampling scheme of step 604 in the second wafer direction rotated 180 degrees relative to the first wafer direction. It should be appreciated that the measurement of the overlay at 180 degrees is similar to the measurement of the overlay at zero degrees (ie, the first rotational position of the sample). Accordingly, the description regarding the measurement of overlay at zero degrees should be construed as extending to step 606.

  In step 608, a first set of TIS values for a set of sub-sampling measurement locations is determined utilizing an overlay measurement taken at 180 degrees and an overlay for the same location measured at zero degrees. Also good. In this approach, the system 200 may apply an algorithm consisting of Equation 1 of the present disclosure to calculate a TIS value for each sub-sampling measurement location.

  In step 610, the second set of TIS values is obtained by approximating the TIS for each set of wafer measurement locations that is not included in the subsampling scheme generated in step 604 using an interpolation process. It may be determined. It is recognized herein that the interpolation process of step 610 is similar to the interpolation process described previously herein. Accordingly, the description of the interpolation process described above should be construed as applying to step 610.

  FIG. 7 is a flow diagram illustrating steps performed in an alternative method that provides device-induced error values across a semiconductor surface using a sub-sampling sampling scheme with smart interpolation. In one aspect, it is recognized that the data processing steps of process flow 700 may be performed via pre-programmed algorithms that are executed by one or more processors of computer system 206. However, one of ordinary skill in the art will recognize that the system 200 should not be construed as limited to the process 700, and that various system configurations may implement the process flow 700.

  In step 702, an overlay measurement may be performed using an omni-directional sampling process at a first wafer orientation on at least a first wafer of a first lot of wafers. It should be appreciated that the all-knowledge sampling process is similar to the above-described all-knowledge sampling process in process flow 600. Accordingly, the omnidirectional sampling description of process 600 should be construed to extend to the present process step 702.

  In step 704, a first set of processes may be generated utilizing one or more results of the overlay measured through the omni-knowledge sampling process of step 702. In one aspect, the first set of process tool correction values may include process tool correction values calculated for each measurement location of each field of at least one wafer. Further, process tool correction values may be calculated for each measurement location on the wafer. For example, the process tool correction value for a given analyzed measurement location may be calculated using a measured overlay of all measurement locations on at least one wafer. In this sense, step 704 operates to generate a set of process tool correction values based on overlay error values obtained from all measurement locations on the wafer.

  In one embodiment, overlay or CD metrology data may be used to calculate an overlay correction value, a capacity correction value, or a focus correction value for each field of the measured wafer. These correction values may then be sent to the lithography tool to improve the performance of the lithography tool. In a general sense, the correction value data may be used to modify the alignment of a lithography tool (eg, stepper) or scanner tool to improve control of subsequent lithographic patterning with respect to overlay performance. .

  Traditionally, overlay errors taken from the field of the wafer may be used to determine the linear overlay function. This linear overlay function may then be used as a correction value for an associated process tool such as a scanner or stepper tool. In addition to the linear overlay function, the higher order nonlinear overlay function may be implemented as an overlay function to calculate a corresponding correction value for a given process tool. For example, an analyzer (eg, KLA-Tencor's KT Analyzer) may be configured to implement a higher order model, and then use the higher order model to straddle the wafer, Overlay and CD metrology data may be entered to calculate correction values on a by field basis. Intrafield correction values may include, but are not limited to, overlay correction values, focus correction values, and volume correction values. The associated table of intrafield correction values generated for each field of the measured wafer may include any correction values known in the art.

  The overlay function used to calculate process tool correction values is generally described in US Pat. No. 7,876,438, issued January 25, 2011, and is incorporated herein by reference.

  In step 706, a second set of processes may be generated utilizing one or more results of the overlay measured through an omni-directional sampling process. In one aspect, the second set of process tool correction values may include process tool correction values calculated for each measurement location for each field of at least one wafer. Further, process tool correction values may be calculated for each measurement location on the wafer. For example, the process tool correction value calculated for a given analyzed measurement location can be obtained using measured overlays of all measurement locations on at least one wafer except the analyzed measurement location. It may be calculated.

  In step 708, the sub-sampling scheme compares the first set of process tool correction values (generated in step 704) with the second set of process tool correction values (generated in step 706). May be generated. In one aspect, as described above, the sub-sampling scheme may include a selected number (N) of measurement locations, the number of locations being selectable by the user. In a further aspect, the N measurement locations that display the largest difference between the first set of process tool correction values and the second set of process tool correction values form a sub-sampling measurement location.

  The measurement location that displays the largest difference between the first set of process tool correction values and the second set of process tool correction values is the creation of a set of measurement locations that display the greatest impact of the correction values. It is contemplated in the specification. In this manner, by focusing on the N measurement locations that have the greatest impact on the process tool correction value, the system 200 analyzes the measurement locations that have little or no effect on the process tool correction value. In this case, the wafer may be sampled more effectively.

  In step 710, the overlay error may be measured in a second wafer scheme rotated 180 degrees with respect to the first wafer direction at each measurement location of the sub-sampling scheme in step 708. Measuring the overlay at 180 degrees is similar to measuring the overlay at zero degrees (ie, the first rotational position of the sample). Accordingly, the above description regarding the measurement of overlay at zero degrees should be construed as extending to step 710.

  In step 712, a first set of TIS values for a set of sub-sampling measurement locations may be determined utilizing an overlay measurement taken at 180 degrees and an overlay for the same location measured at zero degrees. Good. In this manner, system 200 may apply an algorithm consistent with Equation 1 of the present disclosure to calculate a TIS value for each sub-sampling measurement location.

  In step 714, the second set of TIS values approximates the TIS for each set of wafer measurement locations not included in the subsampling scheme generated in step 708 using an interpolation process. May be determined. It is recognized herein that the interpolation process of step 714 is similar to the interpolation process described previously herein. Accordingly, the above description of the interpolation process should be construed as applied to step 714.

  The above process flow 700 is implemented in a manner that determines a sub-sampling scheme of measurement locations that has the greatest impact on the additional overlay matrix, such as, but not limited to, overlay survivor or maximally predicted overlay. It is contemplated herein. The subsampling scheme may consist of measurement locations that have the greatest impact on the combination of overlay matrices, such as, but not limited to, process tool corrections, residuals, or maximum predicted overlays. Further contemplated in the specification.

  All of the methods described herein may include storing the results of one or more step method embodiments in a storage medium. The results may include any results described herein and may be stored in any manner known in the art. Storage media may include any storage media described herein, or any other suitable storage media known in the art. After the results are stored, the results are accessed in a storage medium, used by any method or system embodiment described herein, formatted for display to a user, another software module, method, or It can be used by a system or the like. For example, after the method generates the sub-sampling scheme, the method may include storing the sub-sampling scheme with a measurement method in the storage medium. In addition, the results or output of the embodiments described herein are such as CDSEM, so that the metrology system can use a subsampling scheme for metrology, assuming that the output file can be understood by the metrology system. It may be stored and accessed by the measurement system. Further, the results may be stored “permanently”, “semi-permanently”, temporarily, or for a period of time. For example, the storage medium may be random access memory (RAM) and the results may not necessarily persist permanently in the storage medium.

  It is further contemplated that each embodiment of the above method may include any other step (s) of any other method (s) described herein. In addition, each embodiment of the above method may be performed by any system described herein.

  The state-of-the-art in the art leaves little difference between hardware implementations of system aspects and software implementations, and the use of hardware or software is generally (although not always hardware and software Those skilled in the art will recognize that progress has been made in that the choice between software (in certain concepts that can be important) is a cost-effective trade-off that represents design choices. Like. Thereby, there are various vehicles in which the processes and / or systems and / or other techniques described herein can be effective (eg, hardware, software, and / or firmware) and are preferred. Those skilled in the art will appreciate that the vehicle will vary with the concept that the processes and / or systems and / or other technologies are deployed. For example, if the practitioner determines that speed and accuracy are paramount, the practitioner may choose primarily hardware and / or firmware vehicles, or alternatively if flexibility is paramount The practitioner may primarily choose to implement the software, or alternatively, the practitioner may choose some combination of hardware, software, and / or firmware. Therefore, there are several envisioned vehicles, and any vehicles utilized, that may affect the processes and / or devices and / or other techniques described herein. There is nothing inherently better than a choice that depends on the concepts that are deployed and the practitioner's specific concerns (eg, speed, flexibility, or predictability), all of which may be different. Those skilled in the art will recognize that the optical aspects of implementation typically utilize optically oriented hardware, software, and / or firmware.

  The art may describe devices and / or processes in the manner described herein, and then use engineering schemes to integrate the devices and / or processes thus described into a data processing system. It will be appreciated by those skilled in the art that it is common. That is, at least some of the devices and / or processes described herein can be integrated into a data processing system through a sufficient amount of experimentation. Typical data processing systems generally include one or more system unit enclosures, video displays, memories such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, operating systems, drivers, Graphic user interfaces and computer entities such as application programs, one or more interactive devices such as touchpads or screens, and / or feedback loops and control motors (eg, feedback on sensing position and / or velocity) Those skilled in the art will recognize that the present invention includes a control system that includes a control motor for movement and / or adjustment of components and / or quantities. A typical data processing system may be implemented utilizing any suitable commercially available components, such as those normally found in data computing / communication and / or network computing / communication systems.

  The subject matter described herein refers to different components that are optionally contained within or otherwise coupled to different other components. It should be understood that the architecture shown in this way is exemplary only and in fact many other architectures that achieve the same functionality can be implemented. In the conceptual sense, any arrangement of components to achieve the same functionality is effectively “related” so that the desired functionality is achieved. Thus, any two components herein combined to achieve a particular functionality can be mutually "regardless of architecture or intermediate components so that the desired functionality is achieved. It can be indicated as “related”. Similarly, any two components so related appear to be “operably linked” or “operably coupled” to each other to achieve the desired functionality. Any two components that can be so related can also be seen to be “operably coupled” to each other to achieve the desired functionality. Examples that can be operatively coupled include components that can physically match and / or physically interact, and / or components that can interact wirelessly and / or interact wirelessly, And / or components that interact logically and / or that can logically interact with each other.

  While particular aspects of the subject matter described herein have been shown and described, based on the teachings herein, variations and modifications can be made from the subject matter described herein and its broader aspects. Accordingly, it is intended that the appended claims cover all such changes and modifications as fall within the true spirit and scope of the subject matter described herein. It will be apparent to those skilled in the art that they are encompassed within.

  While specific embodiments of the invention have been shown, it will be apparent that various modifications and embodiments of the invention may be made by those skilled in the art without departing from the scope and spirit of the foregoing disclosure. Accordingly, the scope of the invention should be limited only by the claims appended hereto.

  Many of the disclosures and attendant advantages will be understood by the foregoing description, and various modifications may be made without departing from the disclosed subject matter and without sacrificing all of the advantages of the material. It will be apparent that this may be done in the form, configuration and arrangement of the components. The form described is merely exemplary and it is the intention of the following claims to encompass and include such changes.

Claims (23)

A method for providing a device-induced error value over a semiconductor surface comprising:
Measuring a device-induced error (TIS) on at least one wafer of a lot of wafers through an omni-directional sampling process, wherein the omni-directional sampling process comprises: Measuring, including measuring TIS at a measurement location;
Randomly generating a plurality of sub-sampling schemes, wherein the number of fields sampled in each of the sub-sampling schemes is pre-selected, and each of the randomly generated sub-sampling scheme sets is sampled to have the same number of fields, and generating,
Measuring the TIS at each location of each of the randomly generated subsampling schemes;
Approximating a set of TIS values from each of the randomly generated subsampling schemes to each of the randomly generated subsampling schemes using the TIS measurement, Each set of TIS values for each of the sub-sampling schemes generated in the above is used to generate the randomly generated sub-sampling scheme using the TIS measured at each location of the randomly generated sub-sampling schemes. Approximating, calculated using an interpolation process configured to approximate TIS values for each location not included;
Determining a selected sub-sampling scheme by comparing each of the calculated sets of TIS values with the measured TIS of the omni-directional sampling process, the sub-sampling scheme comprising: Determining, including a set of measurement locations for at least one wafer. Performing subsequent TIS measurements on at least one wafer of subsequent lots of wafers at each of the generated sets of selected sub-sampling measurement locations;
Utilizing one or more interpolation processes, TIS values for each of a set of measurement locations not included in the generated selected sub-sampling scheme of the at least one wafer of the subsequent lot of wafers are obtained. The method of claim 1, further comprising: approximating. Utilizing one or more interpolation processes, TIS values for each of a set of measurement locations not included in the generated selected sub-sampling scheme of the at least one wafer of the subsequent lot of wafers are obtained. The approximation is
Utilizing one or more interpolation processes, TIS values for each of a set of measurement locations not included in the generated selected sub-sampling scheme of the at least one wafer of the subsequent lot of wafers are obtained. 3. The approximation, wherein the part of the interpolation process includes approximating utilizing a trainable history algorithm configured to incorporate information from a previous interpolation process. The method described. Performing subsequent TIS measurements on at least one wafer of a subsequent lot of wafers at one or more measurement locations of the set of selected generated sub-sampling measurement locations;
Calculating an average of two or more TIS values obtained from the set of one or more measurement locations of the generated selected sub-sampling measurement locations;
Assigning the averaged two or more TIS values to all of the one or more measurement locations of the set of selected sub-sampling measurement locations generated. The method described in 1. Each of the calculated set of TIS value, by comparing with the measured TIS of the omniscient sampling process, the method comprising the determined subsampling scheme selected, the sub-sampling method, Said determining comprising a set of measurement locations of said at least one wafer;
The method of claim 1, comprising determining a selected sub-sampling scheme by calculating a difference between each of the calculated sets of TIS values and the measured TIS of the omni-directional sampling process. the method of. Each of the calculated set of TIS value, by comparing with the measured TIS of the omniscient sampling process, the method comprising the determined subsampling scheme selected, the sub-sampling method, wherein it comprises a set of measurements Place at least one wafer, wherein the determining includes
Respectively of the calculated set of TIS value, by calculating the difference between the measured TIS of the omniscient sampling process, comprising: determining a sub-sampling method selected, the preferred sub-sampling method , said configured to provide a difference from the measured TIS and pre-selected level of omniscient sampling process and the approximated TIS below includes determining, in claim 1 The method described.   The method of claim 1, wherein the interpolation process comprises at least one of a spline interpolation process, a polynomial interpolation process, or a neural network interpolation process. Generating a plurality of sub-sampling schemes randomly
The method of claim 1 comprising a Monte Carlo analysis process. A method for providing a device-induced error value over a semiconductor surface comprising:
Generating a device-induced error (TIS) sub-sampling scheme, wherein the TIS sub-sampling scheme is dependent on one or more statistical criteria, a selected number of sampling locations, and TIS dependence across the surface of a semiconductor wafer The TIS sub-sampling scheme is defined to utilize a selected model type and includes a set of measurement locations for the semiconductor wafer; and
Determining a first set of TIS values by measuring TIS at each of the measurement locations of the generated TIS subsampling scheme;
Utilizing a interpolation process to determine a second set of TIS values by approximating the TIS for each of a set of locations not included in the generated TIS subsampling scheme. The interpolation process utilizes the first set of TIS values to calculate an approximate TIS value for each of the sets of locations not included in the generated TIS subsampling scheme. Determining.   The method of claim 9, wherein the generated TIS subsampling scheme includes a subset of available fields of semiconductor wafers in a lot of wafers.   The method of claim 9, wherein the generated TIS subsampling scheme includes a subset of measurement locations of available measurement locations within each field of a semiconductor wafer of a lot of wafers.   The method of claim 9, wherein the number of sampling locations is selectable by a user. The method of claim 9, wherein the model type for TIS dependence across a surface of a semiconductor wafer is user selectable.   The method of claim 9, wherein the generated TIS sub-sampling scheme includes at least one of an optimal sampling scheme, an enhanced sampling scheme, or a reduced sampling scheme.   The method of claim 9, wherein the interpolation process comprises at least one of a spline interpolation process, a polynomial interpolation process, or a neural network interpolation process, or a wavelet-based interpolation process. A method for providing a device-induced error value over a semiconductor surface comprising:
Measuring an overlay on at least one wafer of a lot of wafers in a first wafer direction via an omni-directional sampling process, wherein the omni-directional sampling process comprises: Measuring, including measuring the overlay at the measurement location;
Generating a sub-sampling scheme by identifying a set of measurement locations having an overlay value between a first overlay value and a second overlay value, the sub-sampling scheme comprising: Generating, including a set of measurement locations on a wafer;
Measuring an overlay on the at least one wafer in a second wafer direction rotated 180 degrees relative to the first wafer direction at each of the measurement locations of the sub-sampling scheme;
Using the overlay measured in the first wafer direction and the overlay measured in the second wafer direction rotated 180 degrees with respect to the first wafer direction, the sub-sampling method Determining a first set of instrument-induced error (TIS) values for the set of measurement locations;
Utilizing an interpolation process, a second set of TIS values is obtained by approximating the TIS for each of the set of measurement locations of the at least one wafer not included in the generated subsampling scheme. Determining the interpolation process to calculate an approximate TIS value for each of the sets of locations not included in the generated TIS subsampling scheme. Utilizing a set of determining.   The method of claim 16, wherein the interpolation process comprises at least one of a spline interpolation process, a polynomial interpolation process, or a neural network interpolation process. Said identifying a set of measurement locations having an overlay value between a first overlay value and a second overlay value;
Identifying a set of measurement locations having an overlay value between a first overlay value and a maximum overlay value, wherein the maximum overlay value is the maximum measured for the at least one wafer. The method of claim 16, comprising identifying an overlay value. Said identifying a set of measurement locations having an overlay value between a first overlay value and a second overlay value;
Identifying a set of measurement locations having the largest overlay value of the measurement location measured in the omni-directional sampling process, wherein the set of measurement locations having the largest overlay value is a measurement location 17. The method of claim 16, comprising identifying, including a selected number of. Said identifying a set of measurement locations having an overlay value between a first overlay value and a second overlay value;
Identifying a set of measurement locations having an overlay value between a first overlay value and a minimum overlay value, wherein the minimum overlay value was measured for the at least one wafer The method of claim 16, comprising identifying the minimum overlay value. Said identifying a set of measurement locations having an overlay value between a first overlay value and a second overlay value;
Identifying a set of measurement locations having the smallest overlay value of the measurement location measured within the holistic sampling process, wherein the set of measurement locations having the smallest overlay value 17. The method of claim 16, comprising identifying, including a selected number of. Said identifying a set of measurement locations having an overlay value between a first overlay value and a second overlay value;
Identifying a set of measurement locations having an overlay value between a first overlay value and a second overlay value, wherein the first overlay value and the second overlay value are overlay values The method of claim 16, further comprising: identifying a range, wherein the range of overlay values includes an intermediate overlay value. A method for providing a device-induced error value over a semiconductor surface comprising:
Measuring an overlay on at least one wafer of a lot of wafers in a first wafer direction via an omni-directional sampling process, wherein the omni-directional sampling process comprises: Measuring, including measuring the overlay at the measurement location;
Generating a first set of process tool correction values utilizing one or more results of the overlay measured through the omni-directional sampling process, the first of the process tool correction values A set includes a process tool correction value calculated for each measurement location in each field of the at least one wafer, and a process tool correction value associated with the analyzed measurement location of the at least one wafer. Generating the first set of process tool correction values calculated using the measured overlay of all measurement locations of the at least one wafer;
Generating a second set of process tool correction values utilizing one or more results of the overlay measured through the omni-directional sampling process, wherein the first of the process tool correction values The set of two includes a calculated process tool correction value for each measurement location of each field of the at least one wafer, and a process tool correction value associated with the analyzed measurement location of the at least one wafer. Generating a second set of process tool correction values calculated using the measured overlay of all measurement locations of the at least one wafer, except for the analyzed measurement locations. When,
Generating a sub-sampling scheme by comparing the first generated set of process tool correction values with the second generated set of process tool correction values, the sub-sampling scheme comprising: A set of measurement locations, wherein the set of measurement locations of the sub-sampling scheme includes a selected number of sub-sampling measurement locations, the first generated set of process tool correction values, and a process tool The selected number of measurement locations of the at least one wafer having the largest difference between the second generated set of correction values forms the set of measurement locations of the sub-sampling scheme. Generating,
Measuring an overlay on the at least one wafer in a second wafer direction rotated 180 degrees relative to the first wafer direction at each of the generated sub-sampling measurement locations;
Using the overlay measured in the first wafer direction and the overlay measured in the second wafer direction rotated 180 degrees with respect to the first wafer direction, the sub-sampling method Determining a first set of instrument-induced error (TIS) values for said set of sub-sampling measurement locations;
A second set of TIS values is obtained by approximating the TIS for each of the set of measurement locations of the at least one wafer not included in the generated subsampling scheme using an interpolation process. Wherein the interpolation process calculates the approximate TIS value for each of the sets of locations not included in the generated TIS subsampling scheme. Utilizing the first set.

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